Discover Awesome MCP Servers

note-mcp

Unofficial MCP server for note.com using cookie-based authentication to manage notes and drafts via internal APIs.

Windows CLI MCP Server

A Model Context Protocol server that provides secure command-line access to Windows systems, allowing MCP clients like Claude Desktop to safely execute commands in PowerShell, CMD, and Git Bash shells with configurable security controls.

x402-agent-data

Pay-per-call MCP server offering crypto market signals, web page extraction, and GitHub repo auditing, with automatic settlement in USDC via the x402 protocol.

Octopus Energy MCP Server

Enables retrieval of electricity and gas consumption data from Octopus Energy accounts through their API, with support for date range filtering, pagination, and grouping by time periods.

Reqable Capture Reader MCP Server

Enables AI assistants to query, filter, and analyze local HTTP capture records and REST API test data from the Reqable tool. It provides read-only access to inspect request/response details, headers, and statistics through natural language.

OpenShift LightSpeed MCP Server

Integrates OpenShift LightSpeed with Claude Code to provide AI-powered OpenShift assistance and troubleshooting.

mcp-ad4m

Enables Claude Code with persistent semantic memory, cross-session context, and memory hygiene via a local AD4M executor, offering 13 tools for managing semantic graphs and cross-terminal relay.

MonteWalk

Provides AI agents with institutional-grade quantitative finance tools including real-time market data, paper trading via Alpaca, risk analysis with Monte Carlo simulations, backtesting, and multi-source news sentiment analysis for portfolio management and trading strategy development.

twitter-voice-mcp

An MCP server that analyzes your unique Twitter voice to generate, manage, and post AI-powered tweets and quote tweet drafts. It supports multiple AI providers and provides tools for draft management, voice profiling, and automated content creation from images.

ikaliMCP Server

Provides a secure interface for AI assistants to interact with penetration testing tools like nmap, hydra, sqlmap, and nikto for educational cybersecurity purposes. Includes input sanitization and runs in a Docker container with Kali Linux tools for authorized testing scenarios.

Claude Code AI Collaboration MCP Server

An MCP server that enables multi-provider AI collaboration using models like DeepSeek, OpenAI, and Anthropic through strategies such as parallel execution and consensus building. It provides specialized tools for side-by-side content comparison, quality review, and iterative refinement across different AI providers.

helloworld-demo-mcp

A minimal MCP server offering a hello_world tool that returns a greeting and current UTC time via streamable HTTP.

py-har-mcp

An MCP server for parsing and analyzing HAR files, enabling AI assistants to inspect network traffic with automatic redaction of sensitive authentication headers.

MindMeister MCP Server

Connects Claude to the MindMeister API v2, enabling AI-powered mind map management including viewing, listing, exporting, and sharing maps directly from Claude.

perplexity-mcp-server

Enables interaction with Perplexity AI through MCP tools for chatting, searching, and retrieving documentation.

simplenote-mcp

A remote MCP connector that gives Claude.ai projects a persistent, cross-conversation file store, backed by Simplenote and deployed free on Cloudflare Workers.

mcp-sse-server-demo

Demo del servidor SSE MCP

mcp-docparser

A lightweight document parser MCP server that enables Claude to parse PDFs, Word, Excel, images (OCR), and other document formats with support for chunking and metadata extraction.

Planning Center Online API and MCP Server Integration

Servidor MCP de Planning Center Online

mcp_server

Okay, I can help you outline the steps and provide some code snippets to guide you in implementing a sample MCP (Media Control Protocol) server using a Dolphin MCP client. Keep in mind that this is a simplified example, and a full implementation would require more robust error handling, state management, and feature support. **Conceptual Overview** 1. **Dolphin MCP Client:** This is the application (e.g., a media player, a control panel) that sends MCP commands to the server. We'll assume you have a Dolphin MCP client already available or are using a library that emulates one. 2. **MCP Server:** This is the application you'll build. It listens for incoming MCP connections, parses the commands, performs actions based on those commands, and sends responses back to the client. **Steps to Implement a Sample MCP Server** 1. **Choose a Programming Language and Libraries:** * **Python:** A good choice for rapid prototyping and ease of use. Use the `socket` library for network communication. * **Node.js:** Suitable for asynchronous, event-driven servers. Use the `net` module. * **Java:** A robust option for larger, more complex servers. Use the `java.net` package. * **C#:** Well-suited for Windows environments. Use the `System.Net.Sockets` namespace. For this example, I'll use Python because it's concise and widely accessible. 2. **Set up a Socket Server:** * Create a socket that listens on a specific port (e.g., 9000). * Accept incoming connections from clients. 3. **Receive and Parse MCP Commands:** * Read data from the socket. * Parse the incoming data as MCP commands. You'll need to understand the MCP command format (e.g., command codes, parameters). Refer to the Dolphin MCP documentation for details. * Common MCP commands include: * `PLAY` * `PAUSE` * `STOP` * `SEEK` * `VOLUME` * `STATUS` 4. **Implement Command Handlers:** * Create functions or methods to handle each MCP command. * These handlers will perform the appropriate actions (e.g., start playback, pause playback, set the volume). * For this sample, we'll simulate these actions (e.g., print a message to the console). 5. **Send Responses:** * After processing a command, send a response back to the client. * Responses typically include a status code (e.g., `OK`, `ERROR`) and any relevant data. **Python Example Code** ```python import socket HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 9000 # Port to listen on (non-privileged ports are > 1023) def handle_play(): print("Received PLAY command. Simulating playback...") return "OK: Playing" def handle_pause(): print("Received PAUSE command. Simulating pause...") return "OK: Paused" def handle_stop(): print("Received STOP command. Simulating stop...") return "OK: Stopped" def handle_seek(time): print(f"Received SEEK command. Seeking to {time}...") return f"OK: Seeked to {time}" def handle_volume(level): print(f"Received VOLUME command. Setting volume to {level}...") return f"OK: Volume set to {level}" def handle_status(): print("Received STATUS command. Returning status...") return "OK: Status - Playing" # Replace with actual status def process_command(command): """Parses and executes MCP commands.""" parts = command.split(" ") command_name = parts[0].upper() if command_name == "PLAY": return handle_play() elif command_name == "PAUSE": return handle_pause() elif command_name == "STOP": return handle_stop() elif command_name == "SEEK": if len(parts) > 1: try: time = int(parts[1]) return handle_seek(time) except ValueError: return "ERROR: Invalid time format" else: return "ERROR: Missing time parameter" elif command_name == "VOLUME": if len(parts) > 1: try: level = int(parts[1]) return handle_volume(level) except ValueError: return "ERROR: Invalid volume level" else: return "ERROR: Missing volume level parameter" elif command_name == "STATUS": return handle_status() else: return "ERROR: Unknown command" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) # Receive up to 1024 bytes if not data: break # Client disconnected command = data.decode().strip() # Decode bytes to string and remove leading/trailing whitespace print(f"Received command: {command}") response = process_command(command) conn.sendall(response.encode()) # Encode the response back to bytes print("Server stopped.") ``` **Explanation:** * **`socket.socket()`:** Creates a socket object. * **`s.bind()`:** Binds the socket to a specific address and port. * **`s.listen()`:** Enables the server to accept connections. * **`s.accept()`:** Accepts a connection from a client. Returns a new socket object (`conn`) representing the connection and the client's address (`addr`). * **`conn.recv()`:** Receives data from the client. * **`data.decode()`:** Decodes the received bytes into a string. * **`process_command()`:** Parses the command and calls the appropriate handler function. * **`conn.sendall()`:** Sends data back to the client. * **`data.encode()`:** Encodes the response string back into bytes. **How to Run:** 1. Save the code as a Python file (e.g., `mcp_server.py`). 2. Run the script from your terminal: `python mcp_server.py` **Testing with a Simple Client (netcat)** You can use `netcat` (often abbreviated as `nc`) to simulate a Dolphin MCP client for testing: 1. Open a new terminal window. 2. Connect to the server: `nc localhost 9000` 3. Type MCP commands (e.g., `PLAY`, `PAUSE`, `VOLUME 50`, `STATUS`) and press Enter. 4. You should see the server's responses in the `netcat` terminal and the server's output in the server's terminal. **Important Considerations:** * **Error Handling:** The example code has minimal error handling. You should add more robust error handling to catch exceptions and handle invalid input. * **MCP Specification:** Refer to the official Dolphin MCP documentation for the exact command formats, status codes, and data structures. The example code assumes a simplified command format. * **Threading/Asynchronous Operations:** For a production server, use threading or asynchronous operations (e.g., `asyncio` in Python, `Promises` in Node.js) to handle multiple client connections concurrently. The single-threaded example above will only handle one client at a time. * **Security:** If you're exposing the server to a network, consider security implications (e.g., authentication, authorization). * **State Management:** The server needs to maintain state (e.g., the current playback status, volume level) to respond correctly to commands. * **Real Media Control:** The example only *simulates* media control. To actually control media playback, you'll need to integrate with a media player library or API (e.g., VLC, GStreamer). **Next Steps:** 1. **Study the Dolphin MCP Specification:** This is crucial for understanding the exact command formats and protocols. 2. **Implement More Commands:** Add handlers for all the MCP commands you want to support. 3. **Add Error Handling:** Make the server more robust by handling potential errors. 4. **Implement Concurrency:** Use threading or asynchronous operations to handle multiple clients. 5. **Integrate with a Media Player:** Connect the server to a media player library to control actual media playback. This detailed explanation and code example should give you a solid foundation for building your MCP server. Remember to consult the Dolphin MCP documentation for the most accurate and up-to-date information. Good luck!

typescript-mcp-server

A TypeScript boilerplate for building Model Context Protocol (MCP) servers with example tools (calculator, greet) and resources (system info).

Basic MCP Server

A minimal Model Context Protocol (MCP) server demonstrating the implementation of tools, resources, and prompts. It serves as a starter template built with the Smithery SDK for developing custom integrations.

App Store Connect MCP Server

Enables interaction with Apple's App Store Connect API through natural language to manage apps, beta testing, localizations, analytics, sales reports, and CI/CD workflows for iOS and macOS development.

UniFi Network MCP Server

Enables AI assistants to manage UniFi network infrastructure through 50+ tools covering devices, clients, networks, WiFi, firewall rules, and guest access using the official UniFi Network API.

datalastic-mcp

MCP server that enables AI assistants to access real-time vessel tracking, port information, and maritime data through the Datalastic Marine AIS Data API.

mcp-local-image-reader

A simple MCP server that reads local images and returns them as ImageContent for LLM vision analysis.

Slack MCP Server

A FastMCP-based server that provides complete Slack integration for Cursor IDE, allowing users to interact with Slack API features using natural language.